library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
theme_cowplot2 <- function(...) {
theme_cowplot(font_size = 12, ...) %+replace%
theme(strip.background = element_blank(),
plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
### load all data ---------------------------------
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6 <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_major.yaml")
helper_f2 <- function(x) select(unnest(enframe(x, "subtype", "gene"), cols = gene), gene, subtype)
markers_v6_super <- lapply(yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_super.yaml"), helper_f2)
clrs <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_colors.yaml") %>%
lapply(function(x) map_depth(x, vec_depth(x)-2, unlist))
names(clrs$patient_id) <- str_remove_all(names(clrs$patient_id), "SPECTRUM-OV-")
meta_tbl <- read_excel("_data/small/MSK SPECTRUM - Single cell RNA-seq_v6.xlsx", sheet = 2) %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-")) %>%
filter(therapy == "pre-Rx")
signature_tbl <- read_tsv("_data/small/mutational_signatures_summary.tsv") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-")) %>%
select(patient_id, consensus_signature) %>%
bind_rows(tibble(patient_id = unique(sort(meta_tbl$patient_id[!(meta_tbl$patient_id %in% .$patient_id)])), consensus_signature = "NA")) %>%
mutate(consensus_signature = ordered(consensus_signature, levels = names(clrs$consensus_signature))) %>%
arrange(consensus_signature)
seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
myfeatures <- c("UMAP_1", "UMAP_2", "umapharmony_1", "umapharmony_2", "sample", "RNA_snn_res.0.1", "RNA_snn_res.0.2", "RNA_snn_res.0.3", "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data <- as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy),
by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
RNA_snn_res.0.1 = as.character(RNA_snn_res.0.1),
RNA_snn_res.0.2 = as.character(RNA_snn_res.0.2),
RNA_snn_res.0.3 = as.character(RNA_snn_res.0.3),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj)) %>%
left_join(signature_tbl, by = "patient_id")
patient_id <- sort(unique(plot_data$patient_id))
PTPRC, CD2, CD3D, TRAC, GZMA, NKG7, CD3E, CD3G, CD4, TCF7, CD8A, PRF1, GZMB, CCL5, CCL4, IL32, CD52
markers_v6_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | CD4.T.dysfunctional | CD4.T.naive | CD4.T.reg | CD8.T | Cycling.T.NK | MT.high.T.NK | NK.CD56 | NK.Cytotoxic |
|---|---|---|---|---|---|---|---|---|
| 1 | CD4 | CCR7 | CD4 | CCL4 | CDC20 | IGKC | CD63 | ADGRG1 |
| 2 | CD40LG | CD4 | FOXP3 | CD8A | CDK1 | MALAT1 | CD7 | CX3CR1 |
| 3 | CTLA4 | IL7R | IL2RA | CD8B | MKI67 | MIAT | FCER1G | FCER1G |
| 4 | CXCL13 | KLF2 | TNFRSF4 | CRTAM | PTTG1 | MT-ND6 | GNLY | FCGR3A |
| 5 | FKBP5 | TCF7 | TRAC | GZMA | TOP2A | MTRNR2L12 | KLRC1 | FGFBP2 |
| 6 | IL6ST | GZMB | XIST | KLRD1 | GNLY | |||
| 7 | ITM2A | GZMK | KLRF1 | GZMH | ||||
| 8 | MAF | GZMM | KRT81 | IGFBP7 | ||||
| 9 | NMB | IFNG | KRT86 | KLRD1 | ||||
| 10 | NR3C1 | LAG3 | NCAM1 | KLRF1 | ||||
| 11 | PDCD1 | MT1E | NKG7 | NKG7 | ||||
| 12 | TNFRSF4 | MT1X | TYROBP | PLAC8 | ||||
| 13 | TOX2 | MT2A | XCL1 | PLEK | ||||
| 14 | TSHZ2 | PTMS | XCL2 | PTGDS | ||||
| 15 | TRGC2 | SPON2 | ||||||
| 16 | TYROBP |
marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>%
filter(resolution == louvain_resolution)
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
formattable::formattable(marker_sheet)
| rank | CD4.T.dysfunctional | CD4.T.naive | CD4.T.reg | CD8.T | Cycling.T.NK | doublet.B.cell | doublet.Mast.cell | doublet.Monocyte | doublet.Monocyte_1 | doublet.Plasma.cell | Mito.high | Mito.high_1 | NK.CD56 | NK.Cytotoxic |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CXCL13 | IL7R | TNFRSF4 | CD8A | STMN1 | IGHM | AREG | CST3 | GZMK | SOX4 | XIST | WFDC2 | GNLY | FGFBP2 |
| 2 | NMB | CCR7 | IL2RA | CD8B | MKI67 | MS4A1 | LST1 | S100A9 | STK17A | PTCRA | MTRNR2L12 | SLPI | AREG | FCGR3A |
| 3 | FKBP5 | KLF2 | FOXP3 | CCL4L2 | TUBA1B | MEF2C | IL4I1 | S100A8 | APOE | MAL | MALAT1 | S100A9 | TYROBP | SPON2 |
| 4 | NR3C1 | EEF1B2 | LTB | GZMK | TOP2A | CD79A | TNFSF13B | SPP1 | ATP5F1E | MZB1 | IGKC | S100A8 | KLRC1 | PRF1 |
| 5 | MAF | TPT1 | CTLA4 | CCL5 | CENPF | BANK1 | PCDH9 | FTL | SH2D1A | DNTT | MIAT | CD24 | FCER1G | KLRF1 |
| 6 | IL6ST | EEF1A1 | RTKN2 | GZMH | TUBB | TNFRSF13C | SCN1B | CXCL8 | TMA7 | STMN1 | MT-ND6 | KRT18 | TRDC | CX3CR1 |
| 7 | ITM2A | CD40LG | BATF | CCL4 | HIST1H4C | RALGPS2 | KIT | APOE | NDUFA3 | TFDP2 | NEAT1 | KRT19 | KRT81 | GNLY |
| 8 | CTLA4 | TCF7 | TNFRSF18 | CRTAM | HMGB2 | BCL11A | SPINK2 | LYZ | C1QB | CD1E | MT-ND3 | FTL | KLRD1 | NKG7 |
| 9 | TSHZ2 | GPR183 | SAT1 | TRGC2 | ASPM | MARCH1 | LTC4S | AIF1 | NDUFB1 | AC084033.3 | MT-ND1 | FTH1 | XCL1 | KLRD1 |
| 10 | TNFRSF4 | LTB | TBC1D4 | LAG3 | TYMS | VPREB3 | CTSH | MARCKS | SPP1 | ARPP21 | PTPRC | IFITM3 | XCL2 | PLAC8 |
| 11 | CD40LG | MAL | GADD45A | IFNG | NUSAP1 | TCF4 | LINC00299 | C1QB | HLA-DRB5 | NUCB2 | MT-CO3 | FGFBP2 | IGFBP2 | GZMB |
| 12 | CORO1B | SELL | TIGIT | PTMS | HMGN2 | CD83 | ALDOC | FTH1 | RNF145 | CDK6 | NKTR | KRT8 | CEBPD | PLEK |
| 13 | PDCD1 | LDHB | PMAIP1 | ITM2C | PCLAF | BASP1 | IL1R1 | C15orf48 | ATP5ME | MAP1A | MT-CO1 | SPP1 | CLIC3 | CLIC3 |
| 14 | LIMS1 | SNHG8 | TNFRSF1B | MT1X | H2AFZ | IGHD | TMIGD2 | G0S2 | CD2 | GLUL | MT-ATP6 | CLU | TXK | EFHD2 |
| 15 | CD4 | PABPC1 | IKZF2 | HLA-DPB1 | HIST1H1B | LINC02397 | TOX2 | C1QA | CD3G | MSI2 | N4BP2L2 | SPINT2 | KRT86 | GZMH |
| 16 | RNF19A | NOSIP | UGP2 | GZMA | SMC4 | ADAM28 | KRT81 | FN1 | CCL3L1 | ADA | MT-ND2 | C19orf33 | IL2RB | TYROBP |
| 17 | RBPJ | NOP53 | SOX4 | MT2A | TPX2 | CD22 | GSN | MNDA | EIF3J | AC011893.1 | PCSK7 | FN1 | CTSW | ADGRG1 |
| 18 | ZBED2 | TMEM123 | ICOS | HLA-DRB1 | PCNA | ARHGAP24 | PLAT | BASP1 | PET100 | MIR181A1HG | MT-CYB | CST3 | MATK | CST7 |
| 19 | DUSP4 | LEF1 | LINC01943 | CST7 | UBE2C | SWAP70 | CXXC5 | APOC1 | CD8B | AL357060.1 | CD2 | CLDN3 | KLRB1 | PTGDS |
| 20 | CPM | EIF3E | TNFRSF9 | HLA-DPA1 | CLSPN | LINC00926 | CCR6 | IL1B | BRD9 | GRASP | MT-CO2 | LINC00861 | CCL3 | FCER1G |
| 21 | AC004585.1 | RACK1 | IL32 | LINC02446 | DUT | AFF3 | AFF3 | CSF3R | CD8A | LDLRAD4 | ARID1B | RBP1 | CD7 | IGFBP7 |
| 22 | AHI1 | AQP3 | ARID5B | DTHD1 | SMC2 | CYBB | IL23R | GRN | IL32 | CD1B | AAK1 | MT-ND3 | CD63 | ZEB2 |
| 23 | ARID5B | UBA52 | IFI27 | APOBEC3G | ATAD2 | HVCN1 | DLL1 | MS4A6A | GZMA | CLDN5 | SPOCK2 | GZMH | NKG7 | HOPX |
| 24 | TOX2 | TOMM7 | BIRC3 | CXCR6 | UBE2S | CD24 | ARHGAP10 | CD14 | OTULIN | RCAN1 | IKZF1 | TIMP1 | TMIGD2 | CCL3 |
| 25 | IGFL2 | JUNB | LAYN | JAML | CKS1B | FCRL5 | SCX | C1QC | CST7 | JCHAIN | MT-ND5 | MDK | HOPX | S1PR5 |
| 26 | ICA1 | NACA | CD27 | MT1E | TMPO | FCRL1 | TLE1 | GSN | MALAT1 | TRDC | SF1 | AC004687.1 | TNFRSF18 | KLF2 |
| 27 | BATF | SOCS3 | CORO1B | CCL3L1 | TUBB4B | BLK | C20orf204 | PLAUR | LINC01871 | ID1 | RSRP1 | APOE | SRGAP3 | AKR1C3 |
| 28 | CCDC50 | FXYD5 | TYMP | DUSP2 | KNL1 | TCL1A | IGFBP4 | SPI1 | DDX18 | AC002454.1 | RNF213 | ITGB1 | LAT2 | PRSS23 |
| 29 | TNFRSF18 | SERINC5 | CD4 | COTL1 | CENPE | LY86 | SLC4A10 | RNASE1 | ROMO1 | VIPR2 | CD44 | MT-CYB | GSTP1 | MYBL1 |
| 30 | SRGN | EEF2 | PHACTR2 | THEMIS | PTTG1 | SCIMP | SVIL | CYBB | GPR183 | SOCS2 | CD3G | NUPR1 | KLRC2 | C1orf21 |
| 31 | CD84 | TRABD2A | MIR4435-2HG | CD3D | HELLS | PAX5 | LIF | SERPINA1 | HIST1H4C | CCDC26 | DDX17 | MT-CO1 | LINC00996 | CD247 |
| 32 | RGS1 | TNFRSF25 | CARD16 | GZMM | RRM2 | LINC01781 | CD300LF | FCGR2A | TPM3 | MARCKSL1 | MT-ND4L | S100A13 | CMC1 | ABHD17A |
| 33 | CD200 | ANK3 | ENTPD1 | TNFSF9 | DEK | SPIB | RORC | CD68 | C1QA | NREP | MT-ND4 | MT1G | GZMB | TTC38 |
| 34 | SPOCK2 | RIPOR2 | SPOCK2 | TNIP3 | BIRC5 | TNFRSF13B | CFH | CSF2RA | GIMAP7 | HES4 | PPP2R5C | MT-ND2 | ITGA1 | PTPN12 |
| 35 | TNFRSF25 | ANXA1 | SPATS2L | CD3G | HMGB1 | PKIG | ENPP1 | MS4A7 | AAK1 | GALNT2 | PNISR | MT-ATP6 | NCAM1 | CTSW |
| 36 | SESN3 | FLT3LG | LINC02099 | CLEC2B | DLGAP5 | CD19 | TGM2 | TNFAIP2 | SON | CASC15 | B2M | MT-ND4 | CXXC5 | CEP78 |
| 37 | CHN1 | SARAF | DUSP4 | KLRG1 | NASP | FAM30A | ZG16B | ALDH2 | PDCD4 | APBA2 | CLEC2D | IFI27 | PRF1 | ITGB2 |
| 38 | CH25H | CTSL | STAM | LYST | H2AFX | GNG7 | KIAA1324 | MAFB | SYNRG | TSHR | STK4 | RAB13 | IFITM3 | ARL4C |
| 39 | RILPL2 | AP3M2 | CTSC | RARRES3 | CDK1 | WDFY4 | CA2 | OLR1 | COX7C | YBX3 | ARGLU1 | BEX3 | MCTP2 | XBP1 |
| 40 | ZNRF1 | ZFAS1 | MAGEH1 | FABP5 | H2AFV | CD40 | TNFSF11 | CPVL | ITGB2 | PFKFB2 | FUS | S100A6 | KLRF1 | FLNA |
| 41 | METTL8 | TIMP1 | S100A4 | ZNF683 | CKS2 | FCRL2 | S100A13 | FPR1 | NDUFA1 | TP53INP1 | ACAP1 | MT-CO3 | ZNF683 | PTGDR |
| 42 | PKM | LINC02273 | GLRX | CD27 | FABP5 | BLNK | PTGER3 | SLC11A1 | UQCRB | LEF1 | CD3D | MT-CO2 | SH2D1B | BIN2 |
| 43 | CTSL | TOB1 | PBXIP1 | IKZF3 | MCM7 | COBLL1 | DST | TGFBI | APOC1 | UHRF1 | CXCR4 | RNASE1 | CCL5 | CCL4 |
| 44 | BHLHE40-AS1 | TRADD | AC005224.3 | PPP1R14B | CDKN3 | TLR10 | IRAK3 | IGSF6 | PSME1 | MME | SRSF7 | MT-ND1 | METRNL | LITAF |
| 45 | SLA | EIF4B | BTG3 | EOMES | GAPDH | BTK | KIAA1211L | KCTD12 | CCL4L2 | CHI3L2 | RBM39 | C1QA | MAFF | GK5 |
| 46 | BTLA | TSHZ2 | F5 | APOBEC3C | EZH2 | GAPT | APOL4 | CXCL16 | GZMH | SSBP2 | ANKRD12 | C1QB | CAPN12 | CD300A |
| 47 | PHACTR2 | GIMAP7 | DNPH1 | OASL | GTSE1 | CPNE5 | BCAS1 | RAB31 | ITM2C | SMIM3 | HLA-A | MARCKS | STARD3NL | FGR |
| 48 | MIR155HG | DPP4 | MAF | PDCD1 | HIST1H1D | POU2AF1 | COL4A4 | IFNGR2 | CCL5 | AIF1 | HLA-E | CTSL | SLC16A3 | TXK |
| 49 | CYSLTR1 | NSA2 | HERC5 | YBX3 | RANBP1 | OSBPL10 | NEO1 | CLEC7A | DDX5 | BCL11A | DDX5 | APOC1 | FOS | GZMM |
| 50 | BIRC3 | FAU | DUSP16 | SH3BGRL3 | ZWINT | EBF1 | SCRN1 | CSF1R | TOMM7 | ARRDC2 | HLA-C | MALAT1 | IFITM2 | CHST2 |
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy),
by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
) %>%
left_join(signature_tbl, by = "patient_id")
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -UMAP_1, -UMAP_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))
signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)}
signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
## compute expression module scores
for (i in 1:length(signature_modules)) {
seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
print(paste(names(signature_modules)[i], "DONE"))
}
## [1] "CD8.Cytotoxic DONE"
## [1] "CD8.Dysfunctional DONE"
## [1] "CD8.Naive DONE"
## [1] "CD8.Predysfunctional DONE"
## [1] "ISG.module DONE"
## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
as.matrix %>%
progeny %>%
as.data.frame %>%
as.list
names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
for (i in 1:length(progeny_list)) {
seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
col.name = names(progeny_list)[i])
}
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
marker_top_tbl <- marker_sheet[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy),
by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "Fraction [%]", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "# cells", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2020-12-05
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## bitops 1.0-6 2013-08-17 [2]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## desc 1.2.0 2018-05-01 [2]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## formattable 0.2.0.1 2016-08-05 [1]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## generics 0.0.2 2018-11-29 [2]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.4.0 2019-10-04 [2]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## irlba 2.3.3 2019-02-05 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## knitr 1.26 2019-11-12 [2]
## labeling 0.3 2014-08-23 [2]
## lattice 0.20-38 2018-11-04 [3]
## lazyeval 0.2.2 2019-03-15 [2]
## leiden 0.3.1 2019-07-23 [2]
## lifecycle 0.2.0 2020-03-06 [1]
## listenv 0.8.0 2019-12-05 [2]
## lmtest 0.9-37 2019-04-30 [2]
## lsei 1.2-0 2017-10-23 [2]
## lubridate 1.7.9.2 2020-11-13 [1]
## magrittr * 2.0.1 2020-11-17 [1]
## MASS 7.3-51.5 2019-12-20 [3]
## Matrix 1.2-18 2019-11-27 [3]
## memoise 1.1.0 2017-04-21 [2]
## metap 1.2 2019-12-08 [2]
## mnormt 1.5-5 2016-10-15 [2]
## modelr 0.1.8 2020-05-19 [1]
## multcomp 1.4-12 2020-01-10 [2]
## multtest 2.42.0 2019-10-29 [2]
## munsell 0.5.0 2018-06-12 [2]
## mutoss 0.1-12 2017-12-04 [2]
## mvtnorm 1.0-12 2020-01-09 [2]
## nlme 3.1-143 2019-12-10 [3]
## npsurv 0.4-0 2017-10-14 [2]
## numDeriv 2016.8-1.1 2019-06-06 [2]
## pbapply 1.4-2 2019-08-31 [2]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.0.6 2019-10-09 [2]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.0.2 2018-10-29 [2]
## plotly 4.9.1 2019-11-07 [2]
## plotrix 3.7-7 2019-12-05 [2]
## plyr 1.8.5 2019-12-10 [2]
## png 0.1-7 2013-12-03 [2]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.2 2020-02-09 [1]
## progeny * 1.11.3 2020-10-22 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr * 0.3.4 2020-04-17 [1]
## R.methodsS3 1.7.1 2016-02-16 [2]
## R.oo 1.23.0 2019-11-03 [2]
## R.utils 2.9.2 2019-12-08 [2]
## R6 2.4.1 2019-11-12 [1]
## RANN 2.6.1 2019-01-08 [2]
## rappdirs 0.3.1 2016-03-28 [2]
## RColorBrewer 1.1-2 2014-12-07 [2]
## Rcpp 1.0.4 2020-03-17 [1]
## RcppAnnoy 0.0.16 2020-03-08 [1]
## RcppParallel 4.4.4 2019-09-27 [2]
## Rdpack 0.11-1 2019-12-14 [2]
## readr * 1.4.0 2020-10-05 [1]
## readxl * 1.3.1 2019-03-13 [2]
## rematch 1.0.1 2016-04-21 [2]
## remotes 2.1.0 2019-06-24 [2]
## reprex 0.3.0 2019-05-16 [2]
## reshape2 1.4.3 2017-12-11 [2]
## reticulate 1.14 2019-12-17 [2]
## rlang 0.4.8 2020-10-08 [1]
## rmarkdown 2.0 2019-12-12 [2]
## ROCR 1.0-7 2015-03-26 [2]
## rprojroot 1.3-2 2018-01-03 [2]
## rstudioapi 0.11 2020-02-07 [1]
## rsvd 1.0.3 2020-02-17 [1]
## Rtsne 0.15 2018-11-10 [2]
## rvest 0.3.6 2020-07-25 [1]
## sandwich 2.5-1 2019-04-06 [2]
## scales 1.1.0 2019-11-18 [2]
## sctransform 0.2.1 2019-12-17 [2]
## SDMTools 1.1-221.2 2019-11-30 [2]
## sessioninfo 1.1.1 2018-11-05 [2]
## Seurat * 3.1.2 2019-12-12 [2]
## sn 1.5-4 2019-05-14 [2]
## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
## tsne 0.1-3 2016-07-15 [2]
## usethis 1.5.1 2019-07-04 [2]
## uwot 0.1.5 2019-12-04 [2]
## vctrs 0.3.5 2020-11-17 [1]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## yaml 2.2.1 2020-02-01 [1]
## zoo 1.8-7 2020-01-10 [2]
## source
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Github (clauswilke/colorblindr@1ac3d4d)
## R-Forge (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Github (saezlab/progeny@94bea1c)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library